Abstract
The extent of wildfires cannot be easily mapped using field-based methods in areas with complex topography, and in those areas the use of remote sensing is an alternative. This study first obtained images from the Sentinel-2 satellites for the period 2015–2020 with the objective of applying multi-temporal spectral indices to assess areas burned in wildfires and prescribed fires in the Margalla Hills of Pakistan using the Google Earth Engine (GEE). Using those images, the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), which are often used to assess the severity of fires, were calculated for wildfires and prescribed fires. For each satellite image, spectral indices values were extracted for the 5th, 20th, 40th, 60th, 80th and 95th percentiles of pixels of each burned area. Then, boxplots representing the distribution of these values were plotted for each satellite image to identify whether the regeneration time subsequent to a fire, also known as the burn scar, and the severity of the fire differed between the autumn and summer wildfires, and with prescribed fires. A statistical test revealed no differences for the regeneration time amongst the three categories of fires, but that the severity of summer wildfires was significantly different from that of prescribed fire, and this, for both indices. Second, SAR images were obtained from the Sentinel-1 mission for the same period as that of the optical imagery. A comparison of the response of 34 SAR variables with official data on wildfires and prescribed fires from the Capital Development Authority revealed that the 95th percentile of the Normalized Signal Ratio (NSR p_95) was found to be the best variable to detect fire events, although only 50% of the fires were correctly detected. Nonetheless, when the occurrence of fire events according to the SAR variable NSR p_95 was compared to that from the two spectral indices, the SAR variable was found to correctly identify 95% of fire events. The SAR variable NSR p_95 is thus a suitable alternative to spectral indices to monitor the progress of wildfires and assess their severity when there are limitations to the use of optical images due to cloud coverage or smoke, for instance.
Highlights
Wildfires are a common natural occurrence in many environments and constitute a vital component for their management
A decrease in the Normalized Difference Vegetation Index (NDVI) may sometimes be linked to an increase in water the study area
A decrease in the NDVI may sometimes be linked to an increase in water stress, with the resulting increase in the risk of fire
Summary
Wildfires are a common natural occurrence in many environments and constitute a vital component for their management. Fires of high severity that cause devasting impacts over large areas have recently been documented, notably in the United States of America, the Mediterranean region, Australia, and Pakistan [1,2,3,4,5], bringing into question the effectiveness of current management practices, as these severe wildfires lead to an undesirable alteration and degradation of the landscape [6]. The last factor is important, as it affects the soil’s organic content, which fuels the fire. For this reason, fuel treatments such as vegetation thinning and prescribed fires are commonly conducted in order to alter fuel conditions and make wildfires less severe [11]. This is because a fire leads to changes in vegetation and soil moisture, enabling the use of optical remote sensing images to assess its impact [12,13]
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